With a degree in Data Science, graduates can put their skills to use to solve real-world problems in fields as diverse as genetics, healthcare, e-commerce, finance, government or retail, to name but a few.

The BDatSci programme consists of a set of core modules on all four-year levels. The core modules lay the foundation for studies in the field of data science. For the rest, you have a relatively free choice between the focal areas to enable you to focus on a very specific field within the data science environment. In choosing any additional modules (not part of a focal area), please take note of the stipulations regarding timetable clashes in the general section at the beginning of the various faculty calendars.

Therefore, it is possible within this programme to focus on a specific area of study, called a focal area. You will officially register for BDatSci in the faculty that offers the focal area.

First Year: Core modules (credits, semester)

• Probability Theory and Statistics 114 (16, semester 1)
• Mathematics [Calculus] 114 (16, semester 1)
• Mathematics [Calculus and linear algebra] 144 (16, semester 2)
• Computer Science [Introductory Computer Science] 113 (16, semester 1)
• Computer Science [Introductory Computer Science] 144 (16, semester 2)
• Data Science 141 (16, semester 2)

Second Year: Core modules (credits, semester)

• Mathematical Statistics [Distribution theory and introduction to statistical inference] 214 (16, semester 1)
• Mathematical Statistics [Statistical inference] 245 (8, semester 2)
• Mathematical Statistics [Linear models in Statistics] 246 (8, semester 2)
• Mathematics [Advanced calculus and linear algebra] 214 (16, semester 1)
• Computer Science [Data structures and algorithms] 214 (16, semester 1)
• Computer Science [Computer architecture] 244 (16, semester 2)
• Data Science 241 (16, semester 2)

Third Year: Core modules (credits, semester)

• Mathematical Statistics [Statistical inference and probability theory] 312 (16, semester 1)
• Computer Science [Machine learning] 315 (16, semester 1)
• Computer Science [Data Bases] 34X (16, semester 2)
• Data Science 314 (16, semester 1)
• Data Science 344 (16, semester 2)

Fourth Year: Core modules (credits, semester)

• Introduction to Statistical Learning 441 (12, semester 1)
• Data Science Research assignment 441 (40, semesters 1&2)

Career-focused focal areas

The focal areas are career-driven and module combinations are compulsory within each of these focal areas:

1. Statistical Learning (Faculty of Economic and Management Sciences): Statistical learning entails identifying trends and patterns in data, and using these to construct mathematical models which can be used to predict or classify outcomes. It is used in diverse fields such as computer vision, speech recognition, sport and finance.

Modules in this focal area are mainly from Mathematical Statistics, offered by the Department of Statistics and Actuarial Science.

2. Computer Science (Faculty of Science): Computer Science studies the principles and practice of computation and data processing; it considers problem-solving techniques and data manipulation for everything from routing data over the Internet and powering your social media feeds, to controlling GPS satellites, manufacturing robots, or even your computer.

Modules in this focal area are mainly from Computer Science, offered by the Division of Computer Science.

3. Analytics and Optimisation (Faculty of Economic and Management Sciences): Operations researchers use analytics and optimisation techniques to make a difference in the world. Mathematics is applied to complex problems to find meaningful, data-driven insights and improvements. Operations research is the science of better, evidence-based decision-making.

Modules in this focal area are mainly from Operations Research, offered by the Department of Logistics.

4. Applied Mathematics (Faculty of Science): Applied mathematics looks at real-world applications of mathematical methods in fields such as science, engineering, business, computer science and industry. It is therefore a combination of maths, science and domain knowledge.

Modules in this focal area are mainly from Applied Mathematics, offered by the Division of Applied Mathematics.

5. Behavioural Economics (Faculty of Economic and Management Sciences): Behavioural economics studies how psychological and economic factors affect the decisions we make as investors, consumers, voters and workers. Applying these theories to data provides data scientists with the opportunity to understand, predict and influence human behaviour.

Modules in this focal area are mainly from Economics, offered by the Department of Economics.

6. Statistical Genetics (Faculty of AgriSciences): Statistical genetics is the field of study where statistical methods are used to make inferences of genetic data. It is used in fields such as population quantitative genetics by for example plant breeders and conservation geneticists and in genetic epidemiology where the effects of genes on diseases are studied.

Modules in this focal area are mainly from Biology and Statistical Genetics, offered by the Department of Genetics.

7. Geoinformatics (Faculty of Arts and Social Sciences): Geoinformatics is the science and technology dealing with the structure and character of spatial information, its capture, its classification and qualification, its storage, processing, portrayal and dissemination.

Modules in this focal area are mainly from Geoinformatics, offered by the Department of Geography and Environmental Studies.

8. Statistical Physics (Faculty of Science): Statistical physics uses sophisticated maths and simulations to explore and understand the physics underlying everything from quantum mechanics to phase transitions to factory nuts and bolts.

Modules in this focal area are mainly from Statistical Physics, offered by the Department of Physics.